人工智能与青光眼结构-功能图谱：一项系统综述。
完整摘要 · 中文译文
引言：随着对青光眼结构-功能关系认识的不断深入，基于光学相干断层扫描（OCT）检测到的结构变化开发视野（VF）预测模型已成为重要的研究热点。人工智能（AI）技术已广泛应用于该领域，并展现出巨大潜力。 方法：使用PubMed、Scopus、Web of Science和Embase等数据库进行全面检索，关键检索词包括“人工智能”“机器学习”“深度学习”“光学相干断层扫描”“视野”“结构-功能图”和“青光眼”。经过严格细致的纳入、排除和审查流程，最终共纳入36项研究进行分析。 结果：多项研究报告在预测整体视野指标、逐点视野敏感度或纵向视野进展方面表现良好，尽管其性能因任务、数据集、OCT设备、视野模式、验证设计和性能指标的不同而存在差异。多种可视化技术，包括热图、结构-功能图模拟和遮挡图，已被用于解释这些模型的预测结果。生成的结构-功能图与当前临床对青光眼结构与功能关系的理解相一致。然而，当前AI模型普遍在晚期青光眼中表现下降，并受限于OCT的狭窄扫描范围。 结论：当前的青光眼 基于结构-功能的人工智能预测模型已被应用于逐点视野（VF）预测。然而，为了进一步将这些模型部署到青光眼临床环境中，有必要构建能够基于宽视野容积光学相干断层扫描（OCT）扫描预测模式偏差概率图的模型。
完整摘要 · 英文原文
INTRODUCTION: With advancing insights into the structure-function relationship in glaucoma, developing visual field (VF) predictive models based on optical coherence tomography (OCT)-detected structural changes have emerged as a significant research hotspot. Artificial Intelligence (AI) technologies have been widely applied in this area, demonstrating significant potential. METHODS: A comprehensive search was conducted using databases such as PubMed, Scopus, Web of Science, and Embase with key search terms including "artificial intelligence," "machine learning," "deep learning," "optical coherence tomography," "visual field," "structure-function map," and "glaucoma". Following rigorous and meticulous inclusion, exclusion, and review processes, a total of 36 studies were ultimately selected for analysis. RESULTS: Several studies reported favorable performance for predicting global VF indices, pointwise VF sensitivity, or longitudinal VF progression, although performance varied across tasks, datasets, OCT devices, VF patterns, validation designs, and performance metrics. Various visualization techniques, including heatmaps, simulation of structure-function maps, and occlusion maps, have been employed to interpret the predictions of these models. The generated structure-function maps are consistent with the current clinical understanding of the relationship between glaucoma structure and function. However, current AI models generally suffer from decreased performance in the advanced stage glaucoma and are limited by the narrow scanning range of OCT. CONCLUSIONS: Current glaucoma structure-function AI prediction models have been applied to pointwise VF prediction. However, to further deploy these models in glaucoma clinical settings, it is necessary to construct models that can predict pattern deviation probability maps based on wide-field volumetric OCT scans.